Automatic 3D brain tumor segmentation and classification

ABSTRACT

A fully automatic brain tumor segmentation and classification method and system improve the healthcare experience with machine intelligence. The automatic brain tumor segmentation and classification method and system utilize whole tumor segmentation and multi-class tumor segmentation to provide accurate analysis.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation application of co-pending U.S. patentapplication Ser. No. 15/218,986, filed on Jul. 25, 2016, and titled“AUTOMATIC 3D BRAIN TUMOR SEGMENTATION AND CLASSIFICATION,” which ishereby incorporated by reference in its entirety for all purposes.

FIELD OF THE INVENTION

The present invention relates to tumor imaging. More specifically, thepresent invention relates to 3D brain tumor segmentation andclassification.

BACKGROUND OF THE INVENTION

During neurosurgery, surgeons need to understand where the tumor is andthe boundaries of its various components. Usually, it takes severalhours for clinicians to manually contour the tumor classes from multiplepre-operative MRI scans which is a significant waste of medicalresources.

SUMMARY OF THE INVENTION

A fully automatic brain tumor segmentation and classification method andsystem improve the healthcare experience with machine intelligence. Theautomatic brain tumor segmentation and classification method and systemutilize whole tumor segmentation and multi-class tumor segmentation toprovide accurate analysis.

In one aspect, a method programmed in a non-transitory memory of adevice comprises performing whole tumor segmentation and performingmulti-class tumor segmentation. The whole tumor segmentation includes:data normalization and initial segmentation. The whole tumorsegmentation includes: utilizing multi-modal MRIs including T1, T1c,Flair and T2, determining a least-tumor hemisphere, classifying tissue,identifying intensity control points based on intensities of differenttissue structures, normalizing the intensity for each of the MRIs basedon the intensity control points, locating initial tumor seeds andsegmenting a tumor based on the tumor seeds. The multi-class tumorsegmentation includes: feature extraction, voxel classification andrefinement. Feature extraction includes: voxel-wise features and contextfeatures, wherein voxel-wise features include determining appearancefeatures, texture features and location features. Context featureextraction further includes: extracting a mean intensity in each octantof an MRI, extracting multiscale context features and combining thecontext features from a T1c MRI and a T2 MRI. Voxel classificationutilizes information from the feature extraction and decision trees toclassify a tumor. Refinement includes pathology-guided refinement toensure a correct classification of the tumor.

In another aspect, an apparatus comprises a non-transitory memory forstoring an application, the application for: performing whole tumorsegmentation and performing multi-class tumor segmentation and aprocessing component coupled to the memory, the processing componentconfigured for processing the application. The whole tumor segmentationincludes: data normalization and initial segmentation. The whole tumorsegmentation includes: utilizing multi-modal MRIs including T1, T1c,Flair and T2, determining a least-tumor hemisphere, classifying tissue,identifying intensity control points based on intensities of differenttissue structures, normalizing the intensity for each of the MRIs basedon the intensity control points, locating initial tumor seeds andsegmenting a tumor based on the tumor seeds. The multi-class tumorsegmentation includes: feature extraction, voxel classification andrefinement. Feature extraction includes: voxel-wise features and contextfeatures, wherein voxel-wise features include determining appearancefeatures, texture features and location features. Context featureextraction further includes: extracting a mean intensity in each octantof an MRI, extracting multiscale context features and combining thecontext features from a T1c MRI and a T2 MRI. Voxel classificationutilizes information from the feature extraction and decision trees toclassify a tumor. Refinement includes pathology-guided refinement toensure a correct classification of the tumor.

In another aspect, a system comprises a magnetic resonance imagingdevice and a computing device configured for: performing whole tumorsegmentation and performing multi-class tumor segmentation. The wholetumor segmentation includes: data normalization and initialsegmentation. The whole tumor segmentation includes: utilizingmulti-modal MRIs including T1, T1c, Flair and T2, determining aleast-tumor hemisphere, classifying tissue, identifying intensitycontrol points based on intensities of different tissue structures,normalizing the intensity for each of the MRIs based on the intensitycontrol points, locating initial tumor seeds and segmenting a tumorbased on the tumor seeds. The multi-class tumor segmentation includes:feature extraction, voxel classification and refinement. Featureextraction includes: voxel-wise features and context features, whereinvoxel-wise features include determining appearance features, texturefeatures and location features. Context feature extraction furtherincludes: extracting a mean intensity in each octant of an MRI,extracting multiscale context features and combining the contextfeatures from a T1c MRI and a T2 MRI. Voxel classification utilizesinformation from the feature extraction and decision trees to classify atumor. Refinement includes pathology-guided refinement to ensure acorrect classification of the tumor.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates multi-modal MRIs according to some embodiments.

FIG. 2 illustrates a diagram of the automatic 3D brain tumorsegmentation and classification method according to some embodiments.

FIG. 3 illustrates a diagram of a whole tumor segmentation algorithmflow according to some embodiments.

FIG. 4 illustrates a diagram of a least-tumor hemisphere locatoralgorithm according to some embodiments.

FIG. 5 illustrates a diagram of an initial tumor seeds locator and wholetumor segmentation algorithm according to some embodiments.

FIG. 6 illustrates a diagram of multi-class brain tumor segmentationchallenges.

FIG. 7 illustrates a diagram of voxel-wise features for machine learningaccording to some embodiments.

FIG. 8 illustrates a diagram of utilizing neighborhood information ascontext features according to some embodiments.

FIG. 9 illustrates a diagram of Random Forest classification accordingto some embodiments.

FIG. 10 illustrates a diagram of pathology-guided refinement accordingto some embodiments.

FIG. 11 illustrates a block diagram of an exemplary computing deviceconfigured to implement the automatic 3D brain tumor segmentation andclassification method according to some embodiments.

FIG. 12 illustrates a diagram of a Magnetic Resonance Imaging (MRI)system according to some embodiments.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

To expedite the process of tumor analysis, the automatic 3D brain tumorsegmentation and classification method described herein is able to beimplemented. There are many brain tumor segmentation challenges such aslarge intensity variations across subjects, unclear and irregularboundaries, and significant different tumor appearance in MRI acrosssubject.

FIG. 1 illustrates multi-modal MRIs according to some embodiments. Themulti-modal MRIs include T1, T1 contrast enhanced (T1c), T2 and T2flair.

FIG. 2 illustrates a diagram of the automatic 3D brain tumorsegmentation and classification method according to some embodiments. Inthe step 200, whole tumor segmentation is performed. Whole tumorsegmentation includes data normalization 202 and initial segmentation204. Data normalization 202 includes anatomy-structure-based intensitynormalization. Initial segmentation 204 includes anatomy-guidedgraph-based segmentation. In the step 206, multi-class tumorsegmentation is performed. Multi-class tumor segmentation includesfeature extraction 208, voxel classification 210 and refinement 212.Feature extraction 208 and voxel classification 210 involve using Regionof Interest (ROI)-guided Random Forest. Refinement 212 includespathology-guided refinement. Each of the steps is described furtherherein. In some embodiments, fewer or additional steps are implemented.For example, steps of acquiring the MRI information and/or displayingthe refined images are able to be included. In some embodiments, theorder of the steps is modified.

Unlike the CerebroSpinal Fluid (CSF)-based implementation which assumesthe tumor does not affect the CSF appearance, the automatic 3D braintumor segmentation and classification method uses part of the brain thatcontains no or the least amount of tumors to estimate structureintensities.

FIG. 3 illustrates a diagram of a whole tumor segmentation algorithmflow according to some embodiments. Multi-modal MRIs 300 (e.g., T1, T1c,Flair and T2) are acquired. The T1c MRI is analyzed to determine aleast-tumor hemisphere, in the step 400. Determining a least-tumorhemisphere is able to be implemented in any manner such as locatingtumors in each hemisphere and then comparing the two hemispheres todetermine which has fewer tumors. In another example, the hemispheresare compared with known data (e.g., hemispheres without any tumors), andthe hemisphere that is less different than the known data is selected asthe least-tumor hemisphere. In some embodiments, determining aleast-tumor hemisphere includes determining a symmetric differencebetween the flair and T2 MRIs, since a tumor rarely happens completelysymmetrically, then thresholding and union are performed, and thehemisphere which does not contain the center of mass is the least-tumorhemisphere. In the step 302, tissue classification is implemented toclassify the brain tissue into White Matter (WM), Gray Matter (GM) andCerebrospinal Fluid (CSF). Tissue classification is able to beimplemented in any manner known to one skilled in the art. In the step304, an intensity histogram is generated for each of the multi-modalMRIs 300 (e.g., T1, T1c, Flair, T2). In some embodiments, the intensitycontrol points are generated from the histogram using the median of theCSF, White Matter (WM), Gray Matter (GM), and the corner point. In thestep 306, intensity normalization is performed for each of themulti-modal MRIs 300 (T1, T1c, Flair, T2). Intensity normalization isable to be implemented in any manner. For example, intensities ofcontrol points of each MRI are matched. Furthering the example, theintensities of brain structures (WM, GM, CSF) are mapped to that ofreference MRIs. In the step 500, initial tumor seeds are located. Insome embodiments, the initial tumor seeds are located using theintensity histograms. The initial tumor seeds are able to be detectedbased on intensity (e.g., an intensity above a threshold is consideredto be an initial tumor seed) and symmetric difference of input MRIs. Inthe step 502, a very fast segmentation algorithm based on theregion-growing-based segmentation referred to as GrowCut is implementedfor whole tumor segmentation. In some embodiments, the whole tumorsegmentation is iterated for multiple tumors. In the step 308, using thewhole tumor segmentation and the intensity normalization, tumorclassification is performed for multi-class tumor classification. Forexample, machine learning is utilized to learn to recognize a tumorand/or to classify a tumor. In some embodiments, the tumorclassification takes the output of the whole growth segmentation and theintensity normalization as input to classify the tumor. In someembodiments, fewer or additional steps are implemented. In someembodiments, the order of the steps is modified.

FIG. 4 illustrates a diagram of a least-tumor hemisphere locatoralgorithm according to some embodiments. Determining a least-tumorhemisphere 400 is able to be implemented in any manner such as locatingtumors in each hemisphere and then comparing the two hemispheres todetermine which has fewer tumors. In another example, the hemispheresare compared with known data (e.g., hemispheres without any tumors), andthe hemisphere that is less different than the known data is selected asthe least-tumor hemisphere. In some embodiments, determining aleast-tumor hemisphere includes determining a symmetric differencebetween the flair and T2 MRIs, in the step 402, since a tumor rarelyhappens completely symmetrically, then thresholding and union areperformed, in the step 404, and the hemisphere which does not containthe center of mass is the least-tumor hemisphere. In some embodiments,fewer or additional steps are implemented. In some embodiments, theorder of the steps is modified.

FIG. 5 illustrates a diagram of a whole tumor segmentation algorithmaccording to some embodiments. In the step 504, the intensity histogramfor each of the multi-modal MRIs 300 (T1, T1c, Flair, T2) is analyzed,and thresholds are set based on control points and the symmetricdifference of T2 and Flair are calculated. In the step 506, thresholdingand union is implemented, and in the step 508, thresholding andintersection is implemented. In some embodiments, thresholding and unioninvolves determining a threshold (e.g., based on statistics) andcombining the different MRI images to form a union. In some embodiments,thresholding and intersection involves determining a threshold (e.g.,based on control points) and determining when the different MRI images(e.g., intensity) intersect. The results are combined and thenpost-processing occurs in the step 510 to determine initial tumor seeds512. Using the initial tumor seeds 512, background seeds and targetseeds 514 are identified and used in the GrowCut (or another) algorithm516 to determine a whole tumor 518. In some embodiments, fewer oradditional steps are implemented. In some embodiments, the order of thesteps is modified.

FIG. 6 illustrates a diagram of multi-class brain tumor segmentationchallenges. The brain 600 includes a whole tumor 602, edema 604,non-enhancing core 606, an active core 608, and necrosis 610. Thediagram 650 shows an exemplary formula for modeling complex medicaldecisions based on appearance and pathology rules.

FIG. 7 illustrates a diagram of voxel-wise features for machine learningaccording to some embodiments. The features include appearance features700, texture features 702, and location features 704. The appearancefeatures 700 are able to be determined by smoothing (e.g., Gaussiansmoothing) to de-noise the MRIs. In some embodiments, the smoothing isapplied to the MRIs after the intensity is normalized, such that theresult is a smoothed voxel intensity. The texture features 702 are ableto be analyzed using T2 variance to determine inhomogeneity for adevelop core and a Laplacian of Gaussian (LOG) on T2 to determine edgesand blobs for structure boundary. The location features 704 are alsoable to be analyzed based on the initial segmentation of the wholetumor.

FIG. 8 illustrates a diagram of utilizing neighborhood information ascontext features according to some embodiments. The T1c MRI is rich incore information, and the T2 MRI is rich in boundary interformation.Thus, using the T1c and T2 MRIs, the mean intensity is extracted in eachoctant, multiscale context features are extracted, and the contextfeatures from T1c and T2 are combined.

FIG. 9 illustrates a diagram of Random Forest classification accordingto some embodiments. Using the appearance features 702, texture features704 and location features 706, a voxel-based feature vector 900 isgenerated which is combined with context features, and then traineddecision trees 902 are able to classify the tumor labels.

FIG. 10 illustrates a diagram of pathology-guided refinement accordingto some embodiments. To avoid items being mislabeled such as necrosismislabeled as edema, or non-enhancing cores mislabeled as edema,pathology-guided refinement is utilized. For example, usingcontext-based refinement or graph-based refinement based on pathologyrules (e.g., using GrowCut to grow a non-enhancing core), the analysisis able to be refined to determine an accurate result.

FIG. 11 illustrates a block diagram of an exemplary computing deviceconfigured to implement the automatic 3D brain tumor segmentation andclassification method according to some embodiments. The computingdevice 1100 is able to be used to acquire, store, compute, process,communicate and/or display information such as images and videos. Ingeneral, a hardware structure suitable for implementing the computingdevice 1100 includes a network interface 1102, a memory 1104, aprocessor 1106, I/O device(s) 1108, a bus 1110 and a storage device1112. The choice of processor is not critical as long as a suitableprocessor with sufficient speed is chosen. The memory 1104 is able to beany conventional computer memory known in the art. The storage device1112 is able to include a hard drive, CDROM, CDRW, DVD, DVDRW, HighDefinition disc/drive, ultra-HD drive, flash memory card or any otherstorage device. The computing device 1100 is able to include one or morenetwork interfaces 1102. An example of a network interface includes anetwork card connected to an Ethernet or other type of LAN. The I/Odevice(s) 1108 are able to include one or more of the following:keyboard, mouse, monitor, screen, printer, modem, touchscreen, buttoninterface and other devices. Automatic 3D brain tumor segmentation andclassification application(s) 1130 used to perform the automatic 3Dbrain tumor segmentation and classification method are likely to bestored in the storage device 1112 and memory 1104 and processed asapplications are typically processed. More or fewer components shown inFIG. 11 are able to be included in the computing device 1100. In someembodiments, automatic 3D brain tumor segmentation and classificationmethod hardware 1120 is included. Although the computing device 1100 inFIG. 11 includes applications 1130 and hardware 1120 for the automatic3D brain tumor segmentation and classification method, the automatic 3Dbrain tumor segmentation and classification method is able to beimplemented on a computing device in hardware, firmware, software or anycombination thereof. For example, in some embodiments, the automatic 3Dbrain tumor segmentation and classification method applications 1130 areprogrammed in a memory and executed using a processor. In anotherexample, in some embodiments, the automatic 3D brain tumor segmentationand classification method hardware 1120 is programmed hardware logicincluding gates specifically designed to implement the automatic 3Dbrain tumor segmentation and classification method.

In some embodiments, the automatic 3D brain tumor segmentation andclassification method application(s) 1130 include several applicationsand/or modules. In some embodiments, modules include one or moresub-modules as well. In some embodiments, fewer or additional modulesare able to be included.

Examples of suitable computing devices include a personal computer, alaptop computer, a computer workstation, a server, a mainframe computer,a handheld computer, a personal digital assistant, a cellular/mobiletelephone, a smart appliance, a gaming console, a digital camera, adigital camcorder, a camera phone, a smart phone, a portable musicplayer, a tablet computer, a mobile device, a video player, a video discwriter/player (e.g., DVD writer/player, high definition discwriter/player, ultra high definition disc writer/player), a television,a home entertainment system, an augmented reality device, a virtualreality device, smart jewelry (e.g., smart watch) or any other suitablecomputing device.

FIG. 12 illustrates a diagram of a Magnetic Resonance Imaging (MRI)system according to some embodiments. An MRI device 1200 is used toacquire MRI images such as brain scans. A computing device 1100 receivesthe MRI information (e.g., by downloading or accessing in the cloud)from the MRI device 1200, and the computing device 1100 performs theautomatic 3D brain tumor segmentation and classification method.

To utilize the automatic 3D brain tumor segmentation and classificationmethod described herein, MRI information is analyzed using the automatic3D brain tumor segmentation and classification method. Based upon theanalysis, tumors are able to be identified. Based upon the identifiedtumors, surgeons are able to perform operations to remove the tumors.

In operation, the automatic 3D brain tumor segmentation andclassification method efficiently and accurately identifies tumors inMRI information without user input.

Although the automatic 3D brain tumor segmentation and classificationhas been described herein related to brain tumors, the automatic 3Dbrain tumor segmentation and classification method is able to be adaptedfor any other tumors or maladies.

Some Embodiments of Automatic 3D Brain Tumor Segmentation andClassification

-   1. A method programmed in a non-transitory memory of a device    comprising:    -   performing whole tumor segmentation; and    -   performing multi-class tumor segmentation.-   2. The method of clause 1 wherein the whole tumor segmentation    includes: data normalization and initial segmentation.-   3. The method of clause 1 wherein the whole tumor segmentation    includes:    -   utilizing multi-modal MRIs including T1, T1c, Flair and T2;    -   determining a least-tumor hemisphere;    -   classifying tissue;    -   identifying intensity control points based on intensities of        different tissue structures;    -   normalizing the intensity for each of the MRIs based on the        intensity control points;    -   locating initial tumor seeds; and    -   segmenting a tumor based on the tumor seeds.-   4. The method of clause 1 wherein the multi-class tumor segmentation    includes: feature extraction, voxel classification and refinement.-   5. The method of clause 4 wherein feature extraction includes:    determining voxel-wise features and context features, wherein    voxel-wise features include appearance features, texture features    and location features.-   6. The method of clause 5 wherein context feature extraction further    includes: extracting a mean intensity in each octant of an MRI,    extracting multiscale context features and combining the context    features from a T1c MRI and a T2 MRI.-   7. The method of clause 6 wherein voxel classification utilizes    information from the feature extraction and decision trees to    classify a tumor.-   8. The method of clause 7 wherein refinement includes    pathology-guided refinement to ensure a correct classification of    the tumor.-   9. An apparatus comprising:    -   a non-transitory memory for storing an application, the        application for:        -   performing whole tumor segmentation; and        -   performing multi-class tumor segmentation; and    -   a processing component coupled to the memory, the processing        component configured for processing the application.-   10. The apparatus of clause 9 wherein the whole tumor segmentation    includes: data normalization and initial segmentation.-   11. The apparatus of clause 9 wherein the whole tumor segmentation    includes:    -   utilizing multi-modal MRIs including T1, T1c, Flair and T2;    -   determining a least-tumor hemisphere;    -   classifying tissue;    -   identifying intensity control points based on intensities of        different tissue structures;    -   normalizing the intensity for each of the MRIs based on the        intensity control points;    -   locating initial tumor seeds; and    -   segmenting a tumor based on the tumor seeds.-   12. The apparatus of clause 9 wherein the multi-class tumor    segmentation includes: feature extraction, voxel classification and    refinement.-   13. The apparatus of clause 12 wherein feature extraction includes:    voxel-wise features and context features, wherein voxel-wise    features include determining appearance features, texture features    and location features.-   14. The apparatus of clause 13 wherein context feature extraction    further includes: extracting a mean intensity in each octant of an    MRI, extracting multiscale context features and combining the    context features from a T1c MRI and a T2 MRI.-   15. The apparatus of clause 14 wherein voxel classification utilizes    information from the feature extraction and decision trees to    classify a tumor.-   16. The apparatus of clause 15 wherein refinement includes    pathology-guided refinement to ensure a correct classification of    the tumor.-   17. A system comprising:    -   a magnetic resonance imaging device; and    -   a computing device configured for:        -   performing whole tumor segmentation; and        -   performing multi-class tumor segmentation.-   18. The system of clause 17 wherein the whole tumor segmentation    includes: data normalization and initial segmentation.-   19. The system of clause 17 wherein the whole tumor segmentation    includes:    -   utilizing multi-modal MRIs including T1, T1c, Flair and T2;    -   determining a least-tumor hemisphere;    -   classifying tissue;    -   identifying intensity control points based on intensities of        different tissue structures;    -   normalizing the intensity for each of the MRIs based on the        intensity control points;    -   locating initial tumor seeds; and    -   segmenting a tumor based on the tumor seeds.-   20. The system of clause 17 wherein the multi-class tumor    segmentation includes: feature extraction, voxel classification and    refinement.-   21. The system of clause 20 wherein feature extraction includes:    voxel-wise features and context features, wherein voxel-wise    features include determining appearance features, texture features    and location features.-   22. The system of clause 21 wherein context feature extraction    further includes: extracting a mean intensity in each octant of an    MRI, extracting multiscale context features and combining the    context features from a T1c MRI and a T2 MRI.-   23. The system of clause 22 wherein voxel classification utilizes    information from the feature extraction and decision trees to    classify a tumor.-   24. The system of clause 23 wherein refinement includes    pathology-guided refinement to ensure a correct classification of    the tumor.

The present invention has been described in terms of specificembodiments incorporating details to facilitate the understanding ofprinciples of construction and operation of the invention. Suchreference herein to specific embodiments and details thereof is notintended to limit the scope of the claims appended hereto. It will bereadily apparent to one skilled in the art that other variousmodifications may be made in the embodiment chosen for illustrationwithout departing from the spirit and scope of the invention as definedby the claims.

What is claimed is:
 1. A method comprising: performing whole tumorsegmentation, wherein the whole tumor segmentation comprises: utilizingmulti-modal MRIs; locating initial tumor seeds and segmenting a tumorbased on the tumor seeds; identifying intensity control points based onintensities of different tissue structures; and normalizing theintensity for each of the multi-modal MRIs based on the intensitycontrol points; and performing multi-class tumor segmentation.
 2. Themethod of claim 1 wherein the whole tumor segmentation includes: datanormalization and initial segmentation.
 3. The method of claim 1 whereinthe whole tumor segmentation includes: determining a least-tumorhemisphere; and classifying tissue.
 4. The method of claim 1 wherein themulti-class tumor segmentation includes: feature extraction, voxelclassification and refinement.
 5. The method of claim 4 wherein featureextraction includes: determining voxel-wise features and contextfeatures, wherein voxel-wise features include appearance features,texture features and location features.
 6. The method of claim 5 whereincontext feature extraction further includes: extracting a mean intensityin each octant of an MRI, extracting multiscale context features andcombining the context features from a T1c MRI and a T2 MRI.
 7. Themethod of claim 6 wherein voxel classification utilizes information fromthe feature extraction and decision trees to classify a tumor.
 8. Themethod of claim 7 wherein refinement includes pathology-guidedrefinement to ensure a correct classification of the tumor.
 9. Anapparatus comprising: a non-transitory memory for storing anapplication, the application for: performing whole tumor segmentation,wherein the whole tumor segmentation comprises: utilizing multi-modalMRIs; locating initial tumor seeds and segmenting a tumor based on thetumor seeds; identifying intensity control points based on intensitiesof different tissue structures; and normalizing the intensity for eachof the multi-modal MRIs based on the intensity control points; andperforming multi-class tumor segmentation; and a processing componentcoupled to the memory, the processing component configured forprocessing the application.
 10. The apparatus of claim 9 wherein thewhole tumor segmentation includes: data normalization and initialsegmentation.
 11. The apparatus of claim 9 wherein the whole tumorsegmentation includes: determining a least-tumor hemisphere; andclassifying tissue.
 12. The apparatus of claim 9 wherein the multi-classtumor segmentation includes: feature extraction, voxel classificationand refinement.
 13. The apparatus of claim 12 wherein feature extractionincludes: voxel-wise features and context features, wherein voxel-wisefeatures include determining appearance features, texture features andlocation features.
 14. The apparatus of claim 13 wherein context featureextraction further includes: extracting a mean intensity in each octantof an MRI, extracting multiscale context features and combining thecontext features from a T1c MRI and a T2 MRI.
 15. The apparatus of claim14 wherein voxel classification utilizes information from the featureextraction and decision trees to classify a tumor.
 16. The apparatus ofclaim 15 wherein refinement includes pathology-guided refinement toensure a correct classification of the tumor.
 17. A system comprising: amagnetic resonance imaging device; and a computing device configuredfor: performing whole tumor segmentation, wherein the whole tumorsegmentation comprises: utilizing multi-modal MRIs; locating initialtumor seeds and segmenting a tumor based on the tumor seeds; identifyingintensity control points based on intensities of different tissuestructures; and normalizing the intensity for each of the multi-modalMRIs based on the intensity control points; and performing multi-classtumor segmentation.
 18. The system of claim 17 wherein the whole tumorsegmentation includes: data normalization and initial segmentation. 19.The system of claim 17 wherein the whole tumor segmentation includes:determining a least-tumor hemisphere; and classifying tissue.
 20. Thesystem of claim 17 wherein the multi-class tumor segmentation includes:feature extraction, voxel classification and refinement.
 21. The systemof claim 20 wherein feature extraction includes: voxel-wise features andcontext features, wherein voxel-wise features include determiningappearance features, texture features and location features.
 22. Thesystem of claim 21 wherein context feature extraction further includes:extracting a mean intensity in each octant of an MRI, extractingmultiscale context features and combining the context features from aT1c MRI and a T2 MRI.
 23. The system of claim 22 wherein voxelclassification utilizes information from the feature extraction anddecision trees to classify a tumor.
 24. The system of claim 23 whereinrefinement includes pathology-guided refinement to ensure a correctclassification of the tumor.